Survey-Based Experiments

Gradient provides solutions to business challenges with a custom survey experiment designed around your research needs.

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Find out the true drivers behind the choices
your customers make

What you get

We are constantly making choices, some conscious, but most unconscious. Regular surveys questions with traditional question scales are unable to get to the core of why a consumer makes his or her decision. Most of these scales don’t simulate real-life conditions, which always include some type of real trade-offs. To simulate real life consumer decision making, the most precise approach is through either a Conjoint Analysis or MaxDiff Analysis. Gradient has designed countless experiments within a wide range of markets and sectors, with each its own application.

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Identify the optimal combination of product attributes

purchase

Simulate realastic shopping experiences

insights

Understand how market changes affect consumer behavior

Our tried and tested approach

How it works

1. Divide your product into features

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2. Identify your target audience 

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3. Develop the experiment 

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4. Collect data 

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5. Analyze & report results

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1. Divide your product into features

research

2. Identify your target audience

hypothesis

3. Develop the experiment

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4. Collect data

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5. Analyze & report results

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What you get

Frequently asked questions (FAQ)

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Who invented conjoint analysis?

Mathematicians Duncan Luce and John Tukey published the first source on conjoint in 1964, called ‘Simultaneous conjoint measurement: A new type of fundamental measurement’.

Is conjoint analysis qualitative or quantitative?

Conjoint analysis is a form of quantitative research. Respondents are asked to complete surveys with a number of product concepts which are presented in choice sets.

Why is conjoint analysis used?

Market research helps pre-test products before launch as it is costly to release products into market without testing because of high risk of failure. Whereas non-conjoint research methods are not well-suited for taking into account key market factors (demand and competition),conjoint surveys are use a more realistic methodology which is closer to an actual buying situation.

What is choice-based analysis?

Choice-based analysis (AKA discrete choice experimentation) is a type of response used in conjoint studies where respondents are tasked with choosing which option they would buy. It is considered the most reliable method of choosing responses as it is the most realistic in a market research context.

What is discrete choice analysis?

Discrete choice analysis is examination of datasets that contain choices made by people from among several alternatives. Commonly, we want to understand what drove people to make these choices. For example, how does weather affect people’s choice of eating out, ordering food delivery, or cooking at home. Choice-based conjoint is another example of discrete choice analysis.

What is a partworth?

A partworth (AKA partworth utility or preference score) is a numerical score that measures how much each product feature influences the respondent’s selection of a particular concept.

What are partworth utilities and conjoint simulations used for?

Partworth utilities (AKA preference scores) are useful in describing average preferences for your customers (or sub-groups). For example, you can find that your customers in general prefer a particular colour, flavour or price (vs. another colour/flavour/price). Partworth utilities are the key output of Generic Conjoint because they help with feature selection. Conjoint preference share simulations are useful in showing that percentage of people will choose a particular colour/flavour/price given the choice of other products with different colour/flavour/price. Simulations are the key output of Brand-Specific Conjoint and Brand-Price Trade-Off because they help in predicting adoption, revenue, price elasticity, and cannibalisation.

Can you do segmentation and cluster analysis on conjoint data?

Yes, if you use modern techniques of analysis, such as Hierarchical Bayes, you get individual-level preference scores (model coefficients). These scores can be used in clustering responses and investigating segments of buyers.

Stories from the field

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dce

Conjoint Experiments Explained

Product Guide

Download product guide →
succes-index

How to Define and Measure Success in America

Case study

Download case study →
choice

A Short History of Conjoint Analysis

Whitepaper

Download whitepaper →

Who we’ve partnered with

The world's most forward-looking organizations trust Gradient

harrys
framebridge
bacardi
estee_lauder
populace
oscar
nike
mckinsey__and__compan

Want to learn more?

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